Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.
- Research Organization:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- DE-AC07-05ID14517
- OSTI ID:
- 1013712
- Report Number(s):
- INL/CON-10-20411; TRN: US201110%%790
- Resource Relation:
- Conference: CICS - 2011 IEEE Symposium on Computational Intelligence in Cyber Security,Paris, France,04/11/2011,04/15/2011
- Country of Publication:
- United States
- Language:
- English
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